Generating Space Models and Geometry Models Using a Machine Learning System with Multi-Platform Interfaces

ABSTRACT

Aspects of the disclosure relate to geometry model generation. A computing platform may receive a plurality of drawing models corresponding to different space designs. The computing platform may identify a plurality of design parameters associated with each drawing model of the plurality of drawing models corresponding to the different space designs. The computing platform may train a machine learning engine based on the plurality of drawing models corresponding to the different space designs and the plurality of design parameters associated with each drawing model of the plurality of drawing models corresponding to the different space designs, which may produce at least one geometry model corresponding to the plurality of drawing models. The computing platform may store, in a database storing one or more additional geometry models, the at least one geometry model corresponding to the plurality of drawing models.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of and priority to U.S. ProvisionalPatent Application No. 63/041,535, filed Jun. 19, 2020, and entitled“Generating Space Models and Geometry Models Using a Machine LearningSystem with Multi-Platform Interfaces.” Each of the foregoingapplication(s) is incorporated by reference herein in its entirety.

TECHNICAL FIELD

Aspects of the disclosure relate to digital data processing systems,data processing methods, and machine learning systems. In particular,one or more aspects of the disclosure relate to digital data processingsystems which generate space models and geometry models using machinelearning components and which include multi-platform interfaces toenable interoperability.

BACKGROUND

In some cases, office floorplans and other space models orconfigurations may be created, updated, and/or otherwise modified as newspaces are created, changes in occupancy happen, and/or changes intastes or other preferences occur. In many instances, creating,updating, and/or otherwise modifying a space configuration may require amanual and labor-intensive process which includes selecting designdetails from a plethora of design options. While there have beenattempts to automate this labor-intensive process using computer systemsto generate floor plans automatically, these conventional systems havelargely failed to produce usable results because, among other reasons,there are a large number of variables to be considered simultaneouslywhen creating a workable space configuration, and there are manydifferent ways to document a floorplan or layout, each of which might bedesired and/or needed in a given instance. These conventional systemsalso have implemented inefficient software and hardware, resulting indelayed processing time, increased processing load, and other technicalchallenges.

SUMMARY

Aspects of the disclosure provide technical solutions that overcome oneor more of the technical problems described above and/or other technicalchallenges. For instance, one or more aspects of the disclosure relateto using machine learning techniques in combination with generativedesign algorithms to create and output space models and provide otherfunctionality.

In accordance with one or more embodiments, a computing platform havingat least one processor, a communication interface, and memory mayreceive, via the communication interface, from a first user computingdevice, first space program data identifying one or more parameters of afirst physical space. The computing platform may load a first geometrymodel from a database storing one or more geometry models, which mayinclude information defining a first plurality of design rules. Thecomputing platform may generate a first plurality of space models forthe first physical space based on the first space program dataidentifying the one or more parameters of the first physical space andthe first geometry model. Based on the first geometry model, thecomputing platform may score the first plurality of space modelsgenerated for the first physical space, which may produce a score foreach space model of the first plurality of space models. The computingplatform may rank the first plurality of space models generated for thefirst physical space based on the score for each space model of thefirst plurality of space models, which may produce a ranked list ofspace models. The computing platform may generate user interface datacomprising the ranked list of space models. The computing platform maysend, via the communication interface, to the first user computingdevice, the user interface data comprising the ranked list of spacemodels, which may cause the first user computing device to display auser interface comprising at least a portion of the ranked list of spacemodels.

In some embodiments, the computing platform may receive the first spaceprogram data identifying the one or more parameters of the firstphysical space by receiving information identifying architecturaldetails of the first physical space, organization details for the firstphysical space, work style details for the first physical space, andbudget details for the first physical space. In some embodiments, thecomputing platform may load the first geometry model from the databasestoring the one or more geometry models by selecting the first geometrymodel from a plurality of geometry models generated by the computingplatform using a machine learning engine trained on one or morebest-in-class space designs.

In some embodiments, the computing platform may load the first geometrymodel from the database storing the one or more geometry models byselecting the first geometry model based on the first space program dataidentifying the one or more parameters of the first physical space. Insome embodiments, the computing platform may generate the firstplurality of space models for the first physical space based on thefirst space program data identifying the one or more parameters of thefirst physical space and the first geometry model by: 1) generating aplurality of block models for the first physical space; 2) scoring theplurality of block models generated for the first physical space basedon the first geometry model, which may produce a score for each blockmodel of the plurality of block models; 3) selecting a subset of theplurality of block models based on the score for each block model of theplurality of block models; 4) generating a plurality of settings modelsfor the first physical space, which may each correspond to a particularblock model of the subset of the plurality of block models; 5) scoringthe plurality of settings models generated for the first physical spacebased on the first geometry model, which may produce a score for eachsettings model of the plurality of settings models; 6) selecting asubset of the plurality of settings models based on the score for eachsettings model of the plurality of settings models; 7) generating aplurality of furniture models for the first physical space, where eachfurniture model of the plurality of furniture models corresponds to aparticular settings model of the subset of the plurality of settingsmodels; 8) scoring the plurality of furniture models generated for thefirst physical space based on the first geometry model, which mayproduce a score for each furniture model of the plurality of furnituremodels; and 9) selecting a subset of the plurality of furniture modelsbased on the score for each furniture model of the plurality offurniture models, where the subset of the plurality of furniture modelscorresponds to the first plurality of space models generated for thefirst physical space.

In some embodiments, each block model of the plurality of block modelsmay indicate potential locations of different neighborhoods in the firstphysical space, each settings model of the plurality of settings modelsmay indicate potential locations of different work settings in the firstphysical space, and each furniture model of the plurality of furnituremodels may indicate potential locations of different furniture items inthe first physical space. In some embodiments, the score for each spacemodel of the first plurality of space models may indicate a level ofcompliance with one or more metrics defined by the first geometry model.

In some embodiments, sending the user interface data comprising theranked list of space models to the first user computing device may causethe first user computing device to display one or more of the scoresdetermined for each space model of the first plurality of space models.In some embodiments, the computing platform may receive, via thecommunication interface, from the first user computing device, dataindicating a selection of a first space model from the ranked list ofspace models. In response to receiving the data indicating the selectionof the first space model from the ranked list of space models, thecomputing platform may generate a visual rendering of the first spacemodel. The computing platform may send, via the communication interfaceand to the first user computing device, the visual rendering of thefirst space model, which may cause the first user computing device todisplay a user interface comprising at least a portion of the visualrendering of the first space model.

In some embodiments, the computing platform may receive, via thecommunication interface and from the first user computing device, dataindicating a user modification of the first space model. Based onreceiving the data indicating the user modification of the first spacemodel, the computing platform may update a machine learning engineexecuted on the computing platform.

In some embodiments, the computing platform may receive, via thecommunication interface and from the first user computing device, dataindicating a request to export the first space model to a design tool.In response to receiving the data indicating the request to export thefirst space model to the design tool, the computing platform maygenerate one or more drawing files based on the first space model. Thecomputing platform may send, via the communication interface, to thefirst user computing device, the one or more drawing files generatedbased on the first space model.

In some embodiments, the computing platform may receive, via thecommunication interface and from a second user computing device, secondspace program data identifying one or more parameters of a secondphysical space. The computing platform may load a second geometry modelfrom the database storing the one or more geometry models, which mayinclude information defining a second plurality of design rules. Thecomputing platform may generate a second plurality of space models forthe second physical space based on the second space program dataidentifying the one or more parameters of the second physical space andthe second geometry model. Based on the second geometry model, thecomputing platform may score the second plurality of space modelsgenerated for the second physical space, which may produce a score foreach space model of the second plurality of space models. The computingplatform may rank the second plurality of space models generated for thesecond physical space based on the score for each space model of thesecond plurality of space models, which may produce a second ranked listof space models. The computing platform may generate second userinterface data comprising the second ranked list of space models. Thecomputing platform may send, via the communication interface and to thesecond user computing device, the second user interface data comprisingthe second ranked list of space models, which may cause the second usercomputing device to display a user interface comprising at least aportion of the second ranked list of space models.

In accordance with one or more additional embodiments, a computingplatform having at least one processor, a communication interface, andmemory may receive, via the communication interface, from a data server,a plurality of drawing models corresponding to different space designs.The computing platform may identify a plurality of design parametersassociated with each drawing model of the plurality of drawing modelscorresponding to the different space designs. The computing platform maytrain a machine learning engine based on the plurality of drawing modelscorresponding to the different space designs and the plurality of designparameters associated with each drawing model of the plurality ofdrawing models corresponding to the different space designs, which mayproduce at least one geometry model corresponding to the plurality ofdrawing models. The computing platform may store, in a database storingone or more additional geometry models, the at least one geometry modelcorresponding to the plurality of drawing models.

In some embodiments, in receiving the plurality of drawing modelscorresponding to the different space designs, the computing platform mayreceive at least one two-dimensional computer-aided design (CAD) modelor PDF drawing. In some embodiments, the computing platform may identifythe plurality of design parameters associated with each drawing model ofthe plurality of drawing models corresponding to the different spacedesigns by identifying a plurality of design features, which may includeone or more of: a total square footage, a total number of offices, atotal number of meeting spaces, a total number of community spaces, anumber of seats per office, a number of seats per meeting space, anumber of seats per community space, a percentage of the total squarefootage allocated to offices, a percentage of the total square footageallocated to meeting spaces, a percentage of the total square footageallocated to community space, an average office size, or an averagemeeting space size.

In some embodiments, the computing platform may identify the pluralityof design parameters associated with each drawing model of the pluralityof drawing models corresponding to the different space designs by, priorto identifying the plurality of design parameters, selecting theplurality of design features by applying cognitive machine learningbased on an organization corresponding to each drawing model of theplurality of drawing models. In some embodiments, the computing platformmay select the plurality of design features by selecting the pluralityof design features based on one or more of: an industry, geographicdata, a size, or a personality of the organization.

In some embodiments, the computing platform may select the plurality ofdesign features by selecting the plurality of design features based on auser input, and the plurality of design features may be consistent foreach drawing model of the plurality of drawing models. In someembodiments, the computing platform may produce the at least onegeometry model by identifying one or more design rules that areapplicable to score compliance of at least one space model with theplurality of drawing models, where the one or more design rules includeone or more of data ranges or numerical constraints.

In some embodiments, the computing platform may receive, via thecommunication interface and from a user computing device, space programdata identifying one or more parameters of a physical space. Thecomputing platform may load the at least one geometry model from thedatabase storing the one or more additional geometry models. Thecomputing platform may generate a plurality of space models for thephysical space based on the space program data identifying the one ormore parameters of the physical space and the at least one geometrymodel. The computing platform may score, based on the at least onegeometry model, the plurality of space models generated for the physicalspace, which may produce a score for each space model of the pluralityof space models. The computing platform then may rank the plurality ofspace models generated for the physical space based on the score foreach space model of the plurality of space models, which may produce aranked list of space models. The computing platform may generate userinterface data comprising the ranked list of space models. Then, thecomputing platform may send, via the communication interface and to theuser computing device, the user interface data comprising the rankedlist of space models, which may cause the user computing device todisplay a user interface comprising at least a portion of the rankedlist of space models.

In some embodiments, the computing platform may generate the pluralityof space models for the physical space based on the space program dataidentifying the one or more parameters of the physical space and the atleast one geometry model by: 1) generating a plurality of block modelsfor the physical space; 2) scoring the plurality of block modelsgenerated for the physical space based on the at least one geometrymodel, which may produce a score for each block model of the pluralityof block models; 3) selecting a subset of the plurality of block modelsbased on the score for each block model of the plurality of blockmodels; 4) generating a plurality of settings models for the physicalspace, where each settings model of the plurality of settings modelscorresponds to a particular block model of the subset of the pluralityof block models; 5) scoring the plurality of settings models generatedfor the physical space based on the at least one geometry model, whichmay produce a score for each settings model of the plurality of settingsmodels; 6) selecting a subset of the plurality of settings models basedon the score for each settings model of the plurality of settingsmodels; 7) generating a plurality of furniture models for the physicalspace, where each furniture model of the plurality of furniture modelscorresponds to a particular settings model of the subset of theplurality of settings models; 8) scoring the plurality of furnituremodels generated for the physical space based on the at least onegeometry model, which may produce a score for each furniture model ofthe plurality of furniture models; and 9) selecting a subset of theplurality of furniture models based on the score for each furnituremodel of the plurality of furniture models, where the subset of theplurality of furniture models corresponds to the plurality of spacemodels generated for the physical space.

In some embodiments, each block model of the plurality of block modelsmay indicate potential locations of different neighborhoods in thephysical space, each settings model of the plurality of settings modelsmay indicate potential locations of different work settings in thephysical space, and each furniture model of the plurality of furnituremodels may indicate potential locations of different furniture items inthe physical space.

In accordance with one or more additional embodiments, a computingplatform having at least one processor, a communication interface, andmemory may receive, via the communication interface and from a firstcomputing device, data indicating a request to export a space model to afirst design tool, and the space model may be defined in a plurality ofdata formats. In response to receiving the data indicating the requestto export the space model to the first design tool, the computingplatform may generate one or more first drawing files based on the spacemodel by: 1) selecting, based on the first design tool, a first dataformat of the plurality of data formats, 2) extracting firstformat-specific data from the space model, where the firstformat-specific data is defined in the first data format, and 3)generating the one or more first drawing files using the firstformat-specific data extracted from the space model, where the one ormore first drawing files are generated according to the first dataformat. The computing platform may send, via the communication interfaceand to the first computing device, the one or more first drawing filesgenerated based on the space model.

In some embodiments, the computing platform may receive, from the firstcomputing device, user input defining space information corresponding toone or more elements, or the computing platform may automaticallygenerate space information corresponding to one or more elements usingcognitive machine learning based on best-in-class floor plans. In someembodiments, prior to receiving the data indicating the request toexport the space model to the first design tool, the computing platformmay generate the space model based on the space informationcorresponding to the one or more elements.

In some embodiments, the one or more elements may be one of more of:blocks, settings, or furniture items, where the blocks may be officedepartments, the settings may be room types, and the furniture items maybe individual pieces of furniture. In some embodiments, the computingplatform may send one or more commands directing a client computingdevice to display a graphical user interface that includes a selectablefurniture-purchase element, which may cause the client computing deviceto display the graphical user interface that includes the selectablefurniture-purchase element. Subsequently, the computing platform mayreceive furniture selection information indicating an order for one ormore of the furniture items. The computing platform then may process theorder for the one or more of the furniture items.

In some instances, the plurality of data formats may include one or moreof: computer-aided design (CAD), CET, Revit, or SketchUp. In someinstances, the computing platform may receive, via the communicationinterface and from a second computing device, data indicating a requestto export the space model to a second design tool. In response toreceiving the data indicating the request to export the space model tothe second design tool, the computing platform may generate one or moresecond drawing files based on the space model by: 1) selecting, based onthe second design tool, a second data format of the plurality of dataformats, 2) extracting second format-specific data from the space model,where the second format-specific data is defined in the second dataformat, and 3) generating the one or more second drawing files using thesecond format-specific data from the space model, where the one or moresecond drawing files are generated according to the second data format.The computing platform may send, via the communication interface and tothe second computing device, the one or more second drawing filesgenerated based on the space model.

In some embodiments, the computing platform may generate the space modelby: 1) receiving, via the communication interface and from the firstcomputing device, space program data identifying one or more parametersof a physical space; 2) loading a geometry model from a database storingone or more geometry models, where the geometry model containsinformation defining a plurality of design rules; 3) generating aplurality of block models for the physical space; 4) scoring theplurality of block models generated for the physical space based on thegeometry model, which may produce a score for each block model of theplurality of block models; 5) selecting a subset of the plurality ofblock models based on the score for each block model of the plurality ofblock models; 6) generating a plurality of settings models for thephysical space, where each settings model of the plurality of settingsmodels corresponds to a particular block model of the subset of theplurality of block models; 7) scoring the plurality of settings modelsgenerated for the physical space based on the geometry model, which mayproduce a score for each settings model of the plurality of settingsmodels; 8) selecting a subset of the plurality of settings models basedon the score for each settings model of the plurality of settingsmodels; 9) generating a plurality of furniture models for the physicalspace, where each furniture model of the plurality of furniture modelscorresponds to a particular settings model of the subset of theplurality of settings models; 10) scoring the plurality of furnituremodels generated for the physical space based on the geometry model,which may produce a score for each furniture model of the plurality offurniture models; and 11) selecting a subset of the plurality offurniture models based on the score for each furniture model of theplurality of furniture models, where the subset of the plurality offurniture models includes the space model.

In some embodiments, each block model of the plurality of block modelsmay indicate potential locations of different neighborhoods in thephysical space, each settings model of the plurality of settings modelsmay indicate potential locations of different work settings in thephysical space, and each furniture model of the plurality of furnituremodels may indicate potential locations of different furniture items inthe physical space. In some embodiments, generating the space model mayinclude generating the space model in each of the plurality of dataformats.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is illustrated by way of example and not limitedin the accompanying figures in which like reference numerals indicatesimilar elements and in which:

FIGS. 1A and 1B depict an illustrative operating environment forgenerating space models and geometry models using a machine learningsystem with multi-platform interfaces in accordance with one or moreexample embodiments;

FIGS. 1C, 1D, 1E, 1F, and 1G depict illustrative data structures forvarious models that may be generated, stored, and/or otherwise used inaccordance with one or more example embodiments;

FIGS. 2A-2H depict an illustrative event sequence for generating spacemodels and geometry models using a machine learning system withmulti-platform interfaces in accordance with one or more exampleembodiments;

FIGS. 3-6 depict illustrative user interfaces for generating spacemodels and geometry models using a machine learning system withmulti-platform interfaces in accordance with one or more exampleembodiments;

FIG. 7 depicts an illustrative method for generating space models andgeometry models using a machine learning system with multi-platforminterfaces in accordance with one or more example embodiments;

FIG. 8 depicts an additional illustrative user interface for generatingspace models and geometry models using a machine learning system withmulti-platform interfaces in accordance with one or more exampleembodiments;

FIGS. 9A-9B depict an illustrative event sequence for providingworkplace configuration interfaces in accordance with one or moreexample embodiments; and

FIG. 10 depicts an illustrative workplace configuration interface inaccordance with one or more example embodiments.

DETAILED DESCRIPTION

In the following description of various illustrative embodiments,reference is made to the accompanying drawings, which form a parthereof, and in which is shown, by way of illustration, variousembodiments in which aspects of the disclosure may be practiced. It isto be understood that other embodiments may be utilized, and structuraland functional modifications may be made, without departing from thescope of the present disclosure. Various connections between elementsare discussed in the following description. It is noted that theseconnections are general and, unless specified otherwise, may be director indirect, wired or wireless, and that the specification is notintended to be limiting in this respect.

Some aspects of the disclosure relate to generating space models (whichmay, e.g., also be referred to as space plans, test-fits, and/or floorplans) and geometry models (which may, e.g., also be referred to ascirculation networks or circulation paths) using a machine learningsystem with multi-platform interfaces. For example, a computing platformmay receive space program data, which in some instances may identify oneor more parameters of a physical space. The computing platform may loada geometry model from a database storing one or more geometry models. Insome instances, the geometry models may define a plurality of designrules. Additionally or alternatively, the geometry model may definerules for dividing up a floor plate, placing circulation paths, and/orplacing furniture settings. The computing platform may generate aplurality of space models (e.g., floor plans, test-fits, or other modelsthat are used to document and/or otherwise specify how a space and/orits contents are configured) for the physical space based on the spaceprogram data identifying the one or more parameters of the physicalspace and the geometry model. Based on the geometry model, the computingplatform may score the plurality of space models generated for thephysical space, which may produce a score for each space model of theplurality of space models. The computing platform may rank the pluralityof space models generated for the physical space based on the score foreach space model of the plurality of space models, which may produce aranked list of space models. The computing platform may generate userinterface data comprising the ranked list of space models and may send,via the communication interface and to the user computing device, theuser interface data comprising the ranked list of space models, whichmay cause the user computing device to display a user interfacecomprising at least a portion of the ranked list of space models.

In doing so, the computing platform may automatically generate atargeted series of space models with little, if any, user input.Furthermore, by implementing a generative design algorithm that uses alayered approach to generating the space models (which may, e.g., insome instances include generating and scoring block models (e.g., thatmay be used to locate departments, rooms, spaces, and/or other regionswithin a floor place) and settings models (e.g., that may be used tocreate a configuration for a room or space) in different design stages),the computing platform may reduce processing time and computationalbandwidth. For example, by only solving for settings for a givenphysical space once blocks have been solved for, determined, and/orotherwise defined with respect to the physical space, and by onlysolving for furniture for the physical space once settings have beensolved for, determined, and/or otherwise defined with respect to thephysical space, the computing platform may generate a relatively smallernumber of space models that optimize for and/or fit required parametersand/or non-required, preferred parameters than if the processingrequired to generate models for blocks, settings, and furniture weresimultaneously performed. Accordingly, in at least some instances and byway of example, furniture might only be solved for a subset of settings,and this subset may itself be selected from a subset of blocks, ratherthan solving furniture for all blocks. This tiered, generative designalgorithm provides multiple technical advantages, including reducedprocessing load and reduced consumption of network bandwidth and othercomputing resources. In addition, and in some arrangements that aredescribed in greater detail below, a computing platform implementingsome aspects of the disclosure may generate space models in a pluralityof data formats. In instances where this multi-format approach isimplemented, a computing platform might only generate output elements asingle time at the outset of the modeling process, rather than at theend of the process in response to receiving a request for a space modelor associated data file in an alternate format. When implemented, thismulti-format approach to space model generation may provide additionaltechnical advantages, including reduced processing load and increasingprocessing efficiencies, as well as enhanced interoperability.

FIGS. 1A and 1B depict an illustrative operating environment forgenerating space models and geometry models using a machine learningsystem with multi-platform interfaces in accordance with one or moreexample embodiments. Referring to FIG. 1A, computing environment 100 mayinclude various computer systems, computing devices, networks, and/orother operating infrastructure. For example, computing environment 100may include a generative design computing platform 110, an internal dataserver 120, an external data server 130, a first designer user computingdevice 140, a second designer user computing device 150, a client usercomputing device 160, and a network 170. It should be noted thatcomputing environment 100 is exemplary, and in some cases, a generativedesign computing environment may include more or fewer computer systems,computing devices, networks, and/or other operating interfaces, or maycombine or distribute computing functions into fewer or more devices,and still operate according to methods and principles disclosed herein.

Network 170 may include one or more wired networks and/or one or morewireless networks that interconnect generative design computing platform110, internal data server 120, external data server 130, first designeruser computing device 140, second designer user computing device 150,client user computing device 160, and/or other computer systems and/ordevices. In addition, each of generative design computing platform 110,internal data server 120, external data server 130, first designer usercomputing device 140, second designer user computing device 150, andclient user computing device 160 may be special-purpose computingdevices configured to perform specific functions, as illustrated ingreater detail below, and may include specific computing components suchas processors, memories, communication interfaces, and/or the like.

One or more internal data servers, such as internal data server 120, maybe configured to host and/or otherwise provide internal block models,settings models, furniture models, and/or other data. For instance, theinternal data server 120 may be maintained or otherwise controlled by anenterprise organization that maintains or otherwise controls generativedesign computing platform 110 (e.g., a furniture company, anarchitectural firm, a design firm). In addition, the internal dataserver 120 may be configured to maintain product information,best-in-class floor plans, geometry models, design rules (e.g., designprinciples), and/or other design data developed by, used by, and/orotherwise associated with the enterprise organization.

One or more external data servers, such as external data server 130, maybe configured to host and/or otherwise provide external block models,settings models, furniture models, and/or other data. For instance, theexternal data server 130 may be maintained or otherwise controlled by athird-party organization (e.g., an alternative furniture company, analternative architectural firm, an alternative design firm) differentfrom the enterprise organization that maintains or otherwise controlsgenerative design computing platform 110. In addition, the external dataserver 130 may be configured to maintain product information,best-in-class floor plans, geometry models, design rules, and/or otherdesign data developed by, used by, and/or otherwise associated with thethird-party organization.

First designer user computing device 140 may be configured to be used bya first user (who may, e.g., be an enterprise user associated with anenterprise organization operating generative design computing platform110 such as a designer, architect, or the like). In some instances,first designer user computing device 140 may be configured to presentone or more user interfaces that are generated by and/or otherwiseassociated with a first design tool (e.g., tools related tocomputer-aided design (CAD), CET, Revit, SketchUp, or the like), a localbrowser, and/or one or more other software applications.

Second designer user computing device 150 may be configured to be usedby a second user (who may, e.g., be an enterprise user associated withan enterprise organization operating generative design computingplatform 110 such as a designer, architect, or the like and who may bedifferent from the first user of first designer user computing device140). In some instances, second designer user computing device 150 maybe configured to present one or more user interfaces that are generatedby and/or otherwise associated with a second design tool (e.g., toolsrelated to computer-aided design (CAD), CET, Revit, SketchUp, or thelike) different from the first design tool, a local browser, and/or oneor more other software applications.

Client user computing device 160 may be configured to be used by a thirduser (who may, e.g., be a client or customer of an enterpriseorganization operating generative design computing platform 110 and whomay be different from the first user of first designer user computingdevice 140 and the second user of second designer user computing device150). In some instances, client user computing device 160 may beconfigured to present one or more user interfaces associated with alocal browser that may receive information from, send information to,and/or otherwise exchange information with generative design computingplatform 110 during a browser session. For example, client usercomputing device 160 may be configured to present one or more furniturepurchasing interfaces, floor plan viewing interfaces, design viewinginterfaces, and/or other user interfaces associated with one or morespace models generated by generative design computing platform 110and/or other information received from generative design computingplatform 110.

Referring to FIG. 1B, generative design computing platform 110 mayinclude one or more processor(s) 111, one or more memory(s) 112, and oneor more communication interface(s) 113. In some instances, generativedesign computing platform 110 may be made up of a plurality of differentcomputing devices, which may be distributed within a single data centeror a plurality of different data centers. In these instances, the one ormore processor(s) 111, one or more memory(s) 112, and one or morecommunication interface(s) 113 included in generative design computingplatform 110 may be part of and/or otherwise associated with thedifferent computing devices that form generative design computingplatform 110.

In one or more arrangements, processor(s) 111 may control operations ofgenerative design computing platform 110. Memory(s) 112 may storeinstructions that, when executed by processor(s) 111, cause generativedesign computing platform 110 to perform one or more of the functionsdescribed herein. Communication interface(s) 113 may include one or morewired and/or wireless network interfaces, and communication interface(s)113 may connect generative design computing platform 110 to one or morenetworks (e.g., network 170) and/or enable generative design computingplatform 110 to exchange information and/or otherwise communicate withone or more devices connected to such networks.

In one or more arrangements, memory(s) 112 may store and/or otherwiseprovide a plurality of modules (which may, e.g., include instructionsthat may be executed by processor(s) 111 to cause generative designcomputing platform 110 to perform various functions), databases (whichmay, e.g., store data used by generative design computing platform 110in performing various functions), and/or other elements (which may,e.g., include processing engines, services, and/or other elements). Forexample, memory(s) 112 may store and/or otherwise provide a generativedesign module 112 a, a generative design database 112 b, a geometrymodel engine 112 c, and a machine learning engine 112 d. In someinstances, generative design module 112 a may store instructions thatcause generative design computing platform 110 to generate space modelsand/or execute one or more other functions described herein.Additionally, generative design database 112 b may store data that isused by generative design computing platform 110 in generating spacemodels and/or executing one or more other functions described herein.Geometry model engine 112 c may be used to generate and/or storegeometry models that may be used by generative design module 112 aand/or generative design computing platform 110 in space modelgeneration and ranking. Machine learning engine 112 d may haveinstructions that direct and/or cause the generative design computingplatform 110 to set, define, and/or iteratively refine optimizationrules and/or other parameters used by the generative design computingplatform 110 and/or the other systems in computing environment 100.

FIGS. 1C, 1D, 1E, 1F, and 1G depict illustrative data structures forvarious models that may be generated, stored, and/or otherwise used inaccordance with one or more example embodiments. Referring to FIG. 1C,an example block model 180 is depicted. Block model 180 may, forinstance, include lot dimension data 180 a, exterior walls data 180 b,exterior features data 180 c, interior walls data 180 d, interiorfeatures data 180 e, neighborhood data 180 f (which may e.g., includedepartment information, team information, group information, and/orother information), hallway data 180 g (which may, e.g., includecirculation data regarding hallways, aisles, corridors, stairs,elevators, and/or other areas used to access spaces in a building), andother block data 180 h. Lot dimension data 180 a may, for instance,include information defining one or more dimensions and/or otherfeatures of a lot or other parcel of land where one or more buildingsand/or other structures may be located. Exterior walls data 180 b mayinclude information defining the locations of and/or other features ofone or more exterior walls of such buildings and/or other structures,and exterior features data 180 c may include information defining otherexterior features (e.g., windows, landscaping, exterior columns,decorations, etc.) of such buildings and/or other structures. Interiorwalls data 180 d may include information defining the locations ofand/or other features of one or more interior walls within suchbuildings and/or other structures, and interior features data 180 e mayinclude information defining other interior features (e.g., windows,heating-ventilation-air-conditioning (HVAC) systems and elements,interior columns, restrooms, vertical circulation, mechanical/electricalrooms, closets, etc.). In some instances, these interior and/or exteriorwalls may be two or three dimensional walls that may be dragged,dropped, and/or otherwise modified (e.g., materials may be changed,and/or other modifications may be performed). Neighborhood data 180 fmay include information defining the locations of various organizationdepartments, office neighborhoods, and/or other groupings within aphysical space. Hallway data 180 g may include information defining thelocations of various hallways, walkways, and/or other boundaries in aphysical space, and other block data 180 h may include informationdefining other features of specific areas of the physical space. In someinstances, and as illustrated in greater detail below, some aspects of ablock model may be defined based on input received by generative designcomputing platform 110, such as dimensions and/or exterior features of alot of land or a building located on such a lot, while other aspects ofa block model may be determined by generative design computing platform110 using one or more processes described herein, such as thepositioning and layout of various neighborhoods, hallways, and/or otherblock model features.

Referring to FIG. 1D, an example settings model 182 is depicted.Settings model 182 may, for instance, include a block model 182 a, roomdata 182 b, common (shared) space data 182 c, and other settings data182 d. Block model 182 a may include a block model that has beengenerated and/or stored for a particular physical space (e.g., the samespace to which the settings model 182 applies). For instance, blockmodel 182 may include block model 180 and/or any of its content data.Room data 182 b may include information defining the locations of and/orother features of various rooms (e.g., private offices, meeting rooms,etc.) in a physical space. Common (shared) space data 182 c may includeinformation defining the locations of and/or other features of variouscommon (shared) spaces (e.g., cafes, reception areas, libraries, outdoorpatios, indoor gardens, etc.) in a physical space. Other settings data182 d may include information defining other features of specificsettings within the physical space. In some instances, and asillustrated in greater detail below, some aspects of a settings modelmay be determined by generative design computing platform 110 using oneor more processes described herein, such as the positioning and layoutof various rooms, common (shared) spaces, and/or other settings modelfeatures.

Referring to FIG. 1E, an example furniture model 184 is depicted.Furniture model 184 may, for instance, include a block model 184 a, asettings model 184 b, furniture identification data 184 c, furniturelocation data 184 d, and other furniture data 184 e. Block model 184 amay include a block model that has been generated and/or stored for aparticular physical space (e.g., the same space to which the furnituremodel 184 applies). For instance, block model 184 a may include blockmodel 180 and/or any of its content data. Settings model 184 b mayinclude a settings model that has been generated and/or stored for aparticular physical space (e.g., the same space to which the furnituremodel 184 applies). For instance, settings model 184 b may includesettings model 182 and/or any of its content data. Furnitureidentification data 184 c may include information defining one or morespecific pieces of furniture (e.g., desks, chairs, etc.) for a physicalspace, such as one or more stock keeping units (SKUs) corresponding tosuch pieces of furniture, names and/or other identifiers correspondingto such pieces of furniture, color details and/or other specificationsfor such pieces of furniture, and/or other identifying information.Furniture location data 184 d may include information defining thelocations of one or more specific pieces of furniture within a physicalspace, such as identifiers indicating positioning of desks, chairs,and/or other furniture components at specific work points, coordinatesindicating positioning of each piece of furniture within the physicalspace, and/or other location information. Other furniture data 184 e mayinclude information defining other features of furniture within thephysical space. In some instances, and as illustrated in greater detailbelow, some aspects of a furniture model may be determined by generativedesign computing platform 110 using one or more processes describedherein, such as the inclusion of and positioning of specific pieces offurniture at specific work points within a physical space.

Referring to FIG. 1F, an example space model 186 is depicted. Spacemodel 186 may, for instance, include a block model 186 a, a settingsmodel 186 b, and a furniture model 186 c. Block model 186 a may includea block model that has been generated and/or stored for a particularphysical space (e.g., the same space to which the space model 186applies). For instance, block model 186 a may include block model 180and/or any of its content data. Settings model 186 b may include asettings model that has been generated and/or stored for a particularphysical space (e.g., the same space to which the space model 186applies). For instance, settings model 186 b may include settings model182 and/or any of its content data. Furniture model 186 c may include afurniture model that has been generated and/or stored for a particularphysical space (e.g., the same space to which the space model 186applies). For instance, furniture model 186 c may include furnituremodel 184 and/or any of its content data. In some instances, and asillustrated in greater detail below, some aspects of a space model maybe determined by generative design computing platform 110 using one ormore processes described herein, such as by iteratively generating andoptimizing block models, settings models, and/or furniture models for aspecific physical space.

Referring to FIG. 1G, an example geometry model 188 is depicted.Geometry model 188 may, for instance, include one or more design rulesets, such as design rule set 188 a and design rule set 188 n. Eachdesign rule set may, for instance, include one or more block rules,settings rules, and/or furniture rules. Such block rules, settingsrules, and/or furniture rules may, for instance, be used by generativedesign computing platform 110 in generating and/or optimizing one ormore block models, settings models, and/or furniture models,respectively. For example, design rule set 188 a may include one or moreblock rules 188 a-1, one or more settings rules 188 a-2, and one or morefurniture rules 188 a-3. Block rules 188 a-1 may include informationdefining one or more rules of block layout, block adjacency, and/orother block features. Settings rules 188 a-2 may include informationdefining one or more rules of settings layout, settings adjacency,and/or other settings features. Furniture rules 188 a-3 may includeinformation defining one or more rules of furniture layout, furnituregroupings, and/or other furniture features.

FIGS. 2A-2H depict an illustrative operating environment for generatingspace models and geometry models using a machine learning system withmulti-platform interfaces in accordance with one or more exampleembodiments. Referring to FIG. 2A, at step 201, the generative designcomputing platform 110 may receive one or more drawing models from theinternal data server 120 and/or the external data server 130, which maycorrespond to different space designs (e.g., floor plans, furniturelocation information, best-in-class designs, or the like). For example,in receiving the one or more drawing models, the generative designcomputing platform 110 may receive one or more two-dimensionalcomputer-aided design (CAD) models that may be used to train one or moremachine learning models to identify design parameters and/or todistinguish between different design parameters. In some instances, inreceiving the one or more drawing models, the generative designcomputing platform 110 may receive a quantity of drawing models that issatisfactory and/or sufficient to train the one or more machine learningmodels to distinguish between different room types (e.g., meeting rooms,offices, common spaces, or the like) and/or other design features. Thistraining may, for instance, configure and/or cause the generative designcomputing platform 110 to determine insights and/or relationshipsrelating to square footage, adjacency (which may, e.g., define and/orindicate the proximity and/or location of various departments, settings,rooms, and/or other space features), and/or other typical and/orpreferred features of physical spaces and/or relationships of featuresof physical spaces. In addition to or as an alternative to receiving theone or more drawing models at step 201, the generative design computingplatform 110 may receive, request, or otherwise access photos, videos,and/or other media corresponding to physical spaces and may use thephotos, videos, and/or other media to generate the one or more drawingmodels.

At step 202, the generative design computing platform 110 may identify aplurality of design parameters associated with each drawing model of theplurality of drawing models corresponding to the different spacedesigns. In some instances, the generative design computing platform 110may identify the plurality of design parameters based on user input(which may, e.g., be received at first designer user computing device140, second designer user computing device 150, and/or another computingdevice, and then sent to the generative design computing platform 110).For example, a user may manually identify the design parameters derivedfrom and/or otherwise associated with each drawing model. In theseinstances, in identifying the plurality of design parameters, thegenerative design computing platform 110 may identify a common set ofdesign parameters for each of the plurality of drawing models.Additionally or alternatively, the generative design computing platform110 may apply cognitive machine learning to the plurality of drawingmodels to identify the plurality of design parameters. In theseinstances, the generative design computing platform 110 may identify theplurality of design parameters based on graphical features derived fromthe drawing models and/or metadata linked to the drawing models, such asmetadata information indicating an industry, geographic location, size,personality, and/or other characteristics of an organization linked toeach of the plurality of drawing models. In addition, in theseinstances, the generative design computing platform 110 may identifydifferent design parameters for each of the plurality of drawing models.

In some instances, in identifying the plurality of design parametersassociated with each drawing model of the plurality of drawing modelsreceived at step 201, the generative design computing platform 110 mayidentify a plurality of design features prior to identifying theplurality of design parameters at step 202. These design features may,in some instances, be relatively common for organizations of the samebusiness type (e.g., architecture and design firms may typically occupyspaces having a first common set of features, and these common featuresmay be reflected in drawing models of the spaces occupied by such designfirms, whereas law firms may typically occupy spaces having a secondcommon set of features, and these common features may be reflected indrawing models of the spaces occupied by such law firms). To identify,group, and/or otherwise select these common features from variousdrawing models associated with different types of organizations, thegenerative design computing platform 110 may execute and/or otherwiseuse one or more cognitive machine learning algorithms. For example, thegenerative design computing platform 110 may identify, group, and/orotherwise select the plurality of design features associated with aparticular drawing model of the plurality of drawing models by applyingcognitive machine learning based on the organization and/or occupantcorresponding to the particular drawing model of the plurality ofdrawing models. For instance, the generative design computing platform110 may identify features that may be most applicable to drawing modelsassociated with a specific organization, which in turn may enable thegenerative design computing platform 110 to draw inferences aboutfeatures that may be applicable when creating space models and/orgeometry models for other, similar organizations.

In some cases, for example, the generative design computing platform 110may select the design features based on an industry, geographiclocation, size, personality, and/or other characteristics of anorganization corresponding to each of the plurality of drawing models.For instance, for each organization and/or for each drawing model, thegenerative design computing platform 110 may identify a total squarefootage, a total number of offices, a total number of meeting spaces, atotal number of community spaces, a number of seats per office, a numberof seats per meeting space, a number of seats per community space, apercentage of the total square footage allocated to offices, apercentage of the total square footage allocated to meeting spaces, apercentage of the total square footage allocated to community space, anaverage office size, an average meeting space size, and/or other spacemetrics.

At step 203, the generative design computing platform 110 may train amachine learning engine (e.g., machine learning engine 112 d) based onthe plurality of drawing models corresponding to the different spacedesigns and the plurality of design parameters associated with eachdrawing model of the plurality of drawing models corresponding to thedifferent space designs. In training the machine learning engine, thegenerative design computing platform 110 may produce at least onegeometry model corresponding to the plurality of drawing models. Inparticular, in producing the at least one geometry model, the generativedesign computing platform 110 may determine and/or otherwise produce aset of ranges, numerical constraints, and/or other quantifiable featuresand/or rules that may be applied by the generative design computingplatform 110 in generating a space model for a physical space based onspace program data (e.g., as illustrated in greater detail below).Additionally or alternatively, in producing the at least one geometrymodel, the generative design computing platform 110 may produce alayered model that may have sub-step-specific rules for executingdifferent sub-steps of a generative design process (e.g., block rulesfor executing steps associated with generating a block model, settingsrules for executing steps associated with generating a settings model,furniture rules for executing steps associated with generating afurniture model, and/or other layer-specific rules).

At step 204, the generative design computing platform 110 may store theat least one geometry model. In some instances, the generative designcomputing platform 110 may store the at least one geometry model locally(e.g., in the memory 112 and/or specifically in the generative designdatabase 112 b). Additionally or alternatively, the generative designcomputing platform 110 may store the at least one geometry model at aremote source, such as internal data server 120.

Referring to FIG. 2B, at step 205, the generative design computingplatform 110 may receive space program data from the first designer usercomputing device 140. For example, the generative design computingplatform 110 may receive first space program data identifying one ormore parameters of a first physical space. In some instances, inreceiving the first space program data, the generative design computingplatform 110 may receive information identifying architectural detailsof the first physical space, such as line drawings identifying a shellof a building corresponding to the first physical space, windowlocations, ceiling heights, preferred views, plannable area, elevatorlocations, column locations, entrances, exits, doors, and/or other spacefeatures (which may, e.g., be included in a computer-aided design file).Additionally or alternatively, in receiving the first space programdata, the generative design computing platform 110 may receiveorganization details of an organization that does or will occupy thefirst physical space, such as information indicating a total number ofemployees of the organization, projected growth rate, organizationalbreakdown (e.g., departments, teams, team compositions, relationsbetween teams and/or departments). Additionally or alternatively, inreceiving the first space program data, the generative design computingplatform 110 may receive work style details for the first physicalspace, such as information indicating preferences related to having anopen or closed floor plan, privacy concerns, environmental ambience,and/or other style preferences. Additionally or alternatively, inreceiving the first space program data, the generative design computingplatform 110 may receive budget details for the first physical space,such as information indicating a target and/or maximum price per squarefoot, and/or metric details for the first physical space, such asinformation indicating that some scoring factors are more important thanothers in the overall selection process.

In some instances, this first space program data (which may, e.g., bereceived by generative design computing platform 110 at step 205) may beset by or for an occupant of the first physical space, and may bereceived as user input received via an electronic form or survey. Forexample, the occupant of the first physical space may be prompted (e.g.,by generative design computing platform 110, via one or more graphicaluser interfaces presented on one or more user computing devices) toselect images, word clouds (e.g., graphical representations of wordsand/or groups of words displayed in a cloud format and associated withdifferent themes and/or styles that may indicate different designpreferences), and/or possible design elements that match their visionfor the first physical space. As illustrated below, generative designcomputing platform 110 may use any and/or all of this user input ingenerating a plurality of space models for the first physical space.Additionally or alternatively, in receiving the first space information,the generative design computing platform 110 may receive user inputdefining specific preferences for one or more design elements of thefirst physical space, such as specific preferences for blocks (e.g.,office departments and/or other distinct areas of the physical space),settings (e.g., room types and/or other sub-block features), and/orfurniture items (e.g., individual pieces of furniture and/or othersub-settings features). Additionally or alternatively, in receiving thefirst space information, the generative design computing platform 110may receive information indicating trends in third party data, industrystandards, best-in-class floor plans, and/or other external data. Asalso illustrated below, generative design computing platform 110 may useany and/or all of this information in generating a plurality of spacemodels for the first physical space.

At step 206, the generative design computing platform 110 may load afirst geometry model from a database storing one or more geometry models(e.g., stored in the memory 112 or at the internal data server 120). Inloading the first geometry model, the generative design computingplatform 110 may load information defining a first plurality of designrules that may be part of and/or otherwise associated with the firstgeometry model, such as design rules that control and/or affect thequantities, locations, sizes, and/or other features of various spacemodel design elements, such as blocks, settings, furniture, and/or otherelements (e.g., number of blocks, settings, furniture, and/or otherfeatures; types of blocks, settings, furniture, and/or other features;locations of blocks, settings, furniture, and/or other feature;locations of hallways; and/or other features). In some instances, inloading the first geometry model, the generative design computingplatform 110 may select the first geometry model from a plurality ofgeometry models using a machine learning engine trained on one or morebest-in-class designs. For example, in loading the first geometry model,the generative design computing platform 110 may select a geometry modelthat was generated and/or produced at step 203 using the machinelearning engine 112 d. Additionally or alternatively, in loading thefirst geometry model, the generative design computing platform 110 mayselect the first geometry model based on the first space program data(e.g., received at step 205). For example, the architectural details ofthe physical space, organizational details of the physical space, workstyle details of the physical space, and/or budget details of thephysical space may affect the selection of the first geometry model andthus may be used by generative design computing platform 110 asselection parameters in selecting the first geometry model.

At step 207, the generative design computing platform 110 may generate afirst plurality of space models for the first physical space based onthe first space program data and the first geometry model. For example,the generative design computing platform 110 may generate a plurality ofspace models (which may, e.g., be floor plans that include block models,settings models, and furniture models, as illustrated in greater detailbelow) based on the elements corresponding to the space program datareceived at step 205 and the geometry model loaded at step 206. In someinstances, in generating the first plurality of space models, thegenerative design computing platform 110 may generate each space modelof the first plurality of space models in a plurality of different dataformats. For example, the generative design computing platform 110 maygenerate each space model of the first plurality of space models in aCAD format, a CET format, a Revit format, a SketchUp format, and/or oneor more other formats. As illustrated in greater detail below, bygenerating each space model in different formats at step 207, thegenerative design computing platform 110 may define space models and/orthe elements included in the space models only once at the outset of thedesign process, which may eliminate the need for downstream adjustmentsin formatting to be made and thus may provide improvements in efficiencywhen generating models, editing models, and/or exporting models.

In some instances, in generating the first plurality of space models,the generative design computing platform 110 may generate a plurality ofblock models for the first physical space (which may, e.g., indicate howdepartments and/or furniture settings are arranged on the floor plate).For example, the generative design computing platform 110 may generatethe plurality of block models based on the first space program data andthe first geometry model to determine where different departments of anorganization may be located within the physical space, where hallwaysand/or walls may be located (e.g., between departments), and/or whereand/or how other block level features may be implemented in the physicalspace. In some instances, the geometry model also may include adjacencyrules (e.g., indicating that certain departments should be next to otherdepartments, e.g., there may be rule indicating that the legaldepartment should be next to the accounting department), and thegenerative design computing platform 110 may use and/or account forthese adjacency rules in generating the block models (and/or ingenerating the settings models and/or furniture models, as discussed ingreater detail below). In addition, the various block models that may begenerated by the generative design computing platform 110 may correspondto different variations (e.g., in the locations and/or otherimplementation details of the departments and/or other block-levelfeatures). For example, the generative design computing platform 110 maydetermine that a legal department needs 6,000 square feet and amarketing department needs 20,000 square feet, and the generative designcomputing platform 110 may fit these departments into the first physicalspace in different locations and/or with different variations acrossdifferent block models (which may, e.g., be further refined intodetailed floor plans as settings models and furniture models aregenerated, as described below). In some instances, in generating theplurality of block models for the first physical space, the generativedesign computing platform 110 may take into account existing offices,rooms, and/or other elements in the first physical space that are in afixed location (e.g., elements that are unable to be moved, elementsthat it is preferable and/or not desirable to move (e.g., because ofcost issues, effort issues, and/or other issues), and/or elements thathave characteristics that cause them to be fixed and/or otherwiseimmoveable). In these instances, the generative design computingplatform 110 may incorporate the pre-determined, existing locations ofthese fixed elements into the plurality of block models being generated.

In some instances, in generating the plurality of block models for thefirst physical space, the generative design computing platform 110 mayperform one or more pre-processing steps. For example, in performing theone or more pre-processing steps, the generative design computingplatform 110 may perform a flood fill to create initial assumedlocations for one or more specific departments (e.g., initially placinga legal department in a first portion of the block model, a humanresources department in a second portion of the block model, etc.). Insome instances, the generative design computing platform 110 may performthe flood fill based on known sizes of each department (which may, e.g.,be expressed in terms of area, such as square footage, or in terms ofoccupancy, such as number of people or seats). In some instances, inperforming the one or more pre-processing steps, the generative designcomputing platform 110 may iteratively generate various flood fillsolutions and score the solutions accordingly (e.g., using one or morescoring methods as described below with regard to scoring of the blockmodels).

After generating the plurality of block models, the generative designcomputing platform 110 may score the plurality of block models based onthe first geometry model, which may produce a score for each blockmodel. For instance, the first geometry model may include a plurality ofdesign rules, constraints, and/or metrics that define the ideallocations and/or other properties of block-level features. In scoringeach block model, the generative design computing platform 110 maycompute how closely the particular block model adheres to the designrules, constraints, and/or metrics defined by the geometry model (e.g.,by calculating the distances between the ‘actual’ values of the blockmodel and the ‘ideal’ values of the geometry model, and then subtractingthese distances from a perfect score of 1 or 100). Based on the scoresfor the block models, the generative design computing platform 110 mayselect a subset of the plurality of block models. For example, thegenerative design computing platform 110 may rank the plurality of blockmodels based on their corresponding scores and then select a subset ofthe highest-scoring block models (e.g., the generative design computingplatform 110 may select the block models with the five highest scores).In this way, higher-scoring block models that more closely adhere to the‘ideal’ values defined in the geometry model may be used by thegenerative design computing platform 110 in generating settings models,as illustrated in greater detail below, while the other, lower-scoringblock models may be discarded (which may, e.g., result in technicaladvantages, such as increased computational efficiency, reducedprocessing load, and/or reduced usage of network resources).

For each of the subset of the plurality of block models, the generativedesign computing platform 110 may generate a plurality of settingsmodels, and each settings model may indicate different office orenvironment settings within different blocks, such as the specificlocations of offices, meeting rooms, common (shared) spaces, and/orother settings within different blocks, as well as other features ofthese various settings, such as their size, shape, quantity, intendedpurpose, and/or other features. In addition, each settings model (whichmay, e.g., be generated by the generative design computing platform 110)may correspond to a particular block model of the subset of theplurality of block models. After generating the plurality of settingsmodels, the generative design computing platform 110 may score theplurality of settings models based on the first geometry model (e.g.,using one or more evaluation measures), which may produce a score foreach settings model. Similar to how the first geometry model may includea plurality of design rules, constraints, and/or metrics that define theideal locations and/or other properties of block-level features, asdiscussed above, the first geometry model also may include a pluralityof design rules, constraints, and/or metrics that define the ideallocations and/or other properties of settings-level features. Thus, likewhen scoring the block models, in scoring each settings model, thegenerative design computing platform 110 may compute how closely theparticular settings model adheres to the design rules, constraints,and/or metrics defined by the geometry model (e.g., by calculating thedistances between the ‘actual’ values of the settings model and the‘ideal’ values of the geometry model, and then subtracting thesedistances from a perfect score of 1 or 100). Based on the scores for thesettings models, the generative design computing platform 110 may selecta subset of the plurality of settings models. For example, thegenerative design computing platform 110 may rank the plurality ofsettings models based on their corresponding scores and then select asubset of the highest-scoring settings models (e.g., the generativedesign computing platform 110 may select the settings models with thefive highest scores). In this way, higher-scoring settings models thatmore closely adhere to the ‘ideal’ values defined in the geometry modelmay be used by the generative design computing platform 110 ingenerating furniture models, as illustrated in greater detail below,while the other, lower-scoring settings models may be discarded (whichmay, e.g., result in technical advantages, such as increasedcomputational efficiency, reduced processing load, and/or reduced usageof network resources).

For each of the subset of the plurality of settings models, thegenerative design computing platform 110 may generate a plurality offurniture models, which may indicate which specific pieces of furnitureare to be located in which office or environment settings. In addition,each furniture model (which may, e.g., be generated by the generativedesign computing platform 110) may correspond to a particular settingsmodel of the subset of the plurality of settings models.

After generating the plurality of furniture models, the generativedesign computing platform 110 may score the plurality of furnituremodels based on the first geometry model, which may produce a score foreach furniture model. Similar to how the first geometry model mayinclude a plurality of design rules, constraints, and/or metrics thatdefine the ideal locations and/or other properties of block-levelfeatures and settings-level features, as discussed above, the firstgeometry model also may include a plurality of design rules,constraints, and/or metrics that define the ideal locations and/or otherproperties of furniture-level features. Thus, like when scoring theblock models and the settings models, in scoring each furniture model,the generative design computing platform 110 may compute how closely theparticular furniture model adheres to the design rules, constraints,and/or metrics defined by the geometry model (e.g., by calculating thedistances between the ‘actual’ values of the furniture model and the‘ideal’ values of the geometry model, and then subtracting thesedistances from a perfect score of 1 or 100). Based on the scores for thefurniture models, the generative design computing platform 110 mayselect a subset of the plurality of furniture models. For example, thegenerative design computing platform 110 may rank the plurality offurniture models based on their corresponding scores and then select asubset of the highest-scoring furniture models (e.g., the generativedesign computing platform 110 may select the settings models with thefive highest scores). In addition, the generative design computingplatform 110 may output the selected subset of furniture models as thefirst plurality of space models. In this way, the highest-scoringfurniture models that more closely adhere to the ‘ideal’ values definedin the geometry model may be used by the generative design computingplatform 110 in determining and/or outputting the space models (whichmay, e.g., include complete details for block-level features,settings-level features, and furniture-level features). In addition,this staged and score-based approach (which may, e.g., be implemented bythe generative design computing platform 110 in determining and/oroutputting the space models) may provide various technical advantages,such as increased computational efficiency, reduced processing load,and/or reduced usage of network resources.

In addition, by generating the first plurality of space models using theiterative generative design algorithm illustrated above (e.g., byiteratively generating, scoring, and improving the block models,settings models, and furniture models), generative design computingplatform 110 may generate and output an optimal space model and/or a setof optimal space models in a highly efficient manner. Further, by movingthrough the stage gates illustrated above (e.g., only generatingsettings models once block models have been solved for, and onlygenerating furniture models once settings models have been solved for),the generative design computing platform 110 may reduce consumption ofcomputational bandwidth and achieve faster computing performance. Andthese benefits may be achieved while accounting for both occupant anddesigner preferences (e.g., as indicated in the space program data) anda more agnostic set of design rules (e.g., as defined in the geometrymodel).

In some instances, in generating the first plurality of space models,the generative design computing platform 110 may generate one or moremulti-floor stacking plans (which may, e.g., be floor plans that spanmultiple levels of a building, set of buildings, campus, or otherspace). In these instances, using a similar manner as described abovewith the block models to determine where various departments may fitbest in a particular part of a particular floor, the generative designcomputing platform 110 may identify that one or more specificdepartments should be located on a specific floor for each of aplurality of floors available in the space, and where in the specificfloor each of these departments should fit. In this way, the generativedesign computing platform 110 may place different departments throughoutdifferent floors of a given space, thereby producing a multi-floorstacking plan.

In some instances, using similar methods as described above with regardto the multi-floor stacking plans, the generative design computingplatform 110 may generate block models that span across multiplebuildings and/or other spaces of a campus, which may enable thegenerative design computing platform 110 to perform campus and/or otherlarge scale planning. For example, generative design computing platform110 may place different departments throughout different floors ofdifferent buildings in a given campus, thereby producing a campus planthat may include one or more multi-floor stacking plans (which may,e.g., in turn may include a block model for each floor). In someinstances, the generative design computing platform 110 may generatespace models that involve different building floor plate types. Forinstance, a particular space may have multiple plannable areas on thesame floor of a building (e.g., in two related and/or connected towersof the building), and the generative design computing platform 110 maygenerate models for these different plannable areas using techniquessimilar to those discussed above and/or below (e.g., by placing blocks,settings, and/or furniture in the different plannable areas whileaccounting for other elements already placed in such areas).

At step 208, the generative design computing platform 110 may score thefirst plurality of space models based on the first geometry model. Forexample, the generative design computing platform 110 may calculateand/or otherwise produce a score for each space model based on thedesign rules, constraints, and/or metrics included in the first geometrymodel. In scoring the first plurality of space models, the generativedesign computing platform may identify a level and/or degree ofcompliance of the first plurality of space models with one or moremetrics defined by the first geometry model. For example, the firstgeometry model may include the one or more metrics and the generativedesign computing platform 110 may calculate and/or otherwise assess towhat degree the first plurality of space models are in compliance withthe first geometry model (e.g., by calculating one or more distances, asdescribed in the examples above with respect to the block models,settings models, and furniture models that may provide the basis forand/or make up the space models; and then summing and/or averaging suchdistance values). In some instances, the geometry model may includemetrics such as views to outside and/or preferred views, daylight,setting suitability (e.g., an evaluation of each furniture setting andwhether it is placed in a suitable area—for example, are work cafesplaced near high-trafficked areas/workstations placed in quiet areas?),space syntax, aggregate compliance, adjacencies (which may, e.g.,include one or more rules defining that one or more specific departmentsshould preferably be located next to or within a predetermined distanceof one or more other specific departments, such as a rule specifyingthat a product management department should be located next to anengineering department or a rule specifying that a legal departmentshould be located next to an accounting department), and/orbuzz/distraction (which may, e.g., include one or more rules forbalancing chance social encounters among occupants of the space withpotential distractions encountered or experienced by occupants of thespace because of certain layout features). In these instances, inscoring the first plurality of space models, the generative designcomputing platform 110 may calculate and/or otherwise produce a scorefor each space model based on how well each space model providesfeatures aligned with these metrics.

For example, in scoring the first plurality of space models, thegenerative design computing platform 110 may quantify and/or otherwiseassess the views to the outside, preferred views, and/or access todaylight metrics by identifying, for each work point in a space model, aline from a chair at the work point to a window in the physical space(e.g., as indicated in the given space model), computing a distance ofthe line, and identifying if any objects are in between the work point(e.g., a chair, a sofa, a seat, or the like) and the window (e.g., awall, partition, or the like) or if the distance exceeds a predeterminedthreshold (e.g., if the distance is too far for a person located at thework point to enjoy the view). In making this assessment, the generativedesign computing platform 110 also may take into account what the viewfrom the given work point includes (e.g., a view of a courtyard may bemore desirable than a view of a parking lot or a wall of a neighboringbuilding). The generative design computing platform 110 also may takeinto account a position of the building relative to the sun. Any and/orall of these considerations may be quantified in accordance with themetrics and may be used by the generative design computing platform 110in scoring each space model of the first plurality of space models.

As another example, in scoring the first plurality of space models, thegenerative design computing platform 110 may quantify and/or otherwiseassess the setting suitability for each work point in a space modeland/or each setting in a space model by identifying, for the given workpoint or setting, surroundings of the work point or setting anddetermining whether and/or to what extent the work point or settingcomplies with the rules of the settings model. For example, in makingsuch an assessment, the generative design computing platform 110 may, insome instances, determine occupancy for a given space, predict a decibellevel in the space based on the predicted occupancy, and identify howfar away from the space an office or other work point should be, basedon the decibel level, so as to maintain a quiet office or work point.

As another example, in scoring the first plurality of space models, thegenerative design computing platform 110 may quantify, assess, and/orotherwise score the space syntax for a given space model by identifyingpredicted traffic patterns in the physical space in view of the layoutof the space model (e.g., how many turns to move from one location toanother location in the space, how clear are corridors in the space, howadjacent are related teams, how well does the space providepossibilities for chance encounters, and/or other space syntax factors).For example, in scoring a given space model, the generative designcomputing platform 110 may balance maintaining short distances betweenfrequently visited portions of the physical space for variousindividuals against allowing individuals in the space to experiencechance encounters (e.g., it may be desirable for everything located inthe space to be conveniently accessible to people affiliated withdifferent teams, while still allowing people affiliated with differentteams to encounter someone from another team on occasion). Afterquantifying and/or otherwise assessing one or more of the featuresdescribed above, the generative design computing platform 110 maycalculate and/or otherwise determine a score for each metric withrespect to each space model of the first plurality of space models(e.g., 1-10, or the like). The generative design computing platform 110then may, for instance, compute an aggregate score for each space modelof the first plurality of space models by computing an average of themetric scores determined for the particular space model.

Referring to FIG. 2C, at step 209, the generative design computingplatform 110 may rank the first plurality of space models based on thescore for each space model (e.g., the scores produced at step 208). Indoing so, the generative design computing platform 110 may produce afirst ranked list of space models for the first physical space. In someinstances, the generative design computing platform 110 may rank thefirst plurality of space models based on the metric scores for eachspace model and/or the aggregate score for each space model.

At step 210, the generative design computing platform 110 may generatefirst user interface data that includes the first ranked list of spacemodels produced at step 209. The first user interface data generated bythe generative design computing platform 110 may define one or moreportions of a graphical user interface, such as the user interfacedescribed in greater detail below in connection with FIG. 3. At step211, the generative design computing platform 110 may send, via thecommunication interface 113, the first user interface data to the firstdesigner user computing device 140. In some instances, by sending thefirst user interface data to the first designer user computing device140, the generative design computing platform 110 may cause the firstdesigner user computing device 140 to display a user interface thatincludes at least a portion of the first ranked list of space models. Insome instances, sending the user interface data to the first designeruser computing device 140 may cause the first designer user computingdevice 140 to display one or more of the scores computed for each spacemodel at step 208 (e.g., the metric scores, aggregate scores, and/orother scores discussed in the examples above).

At step 212, the first designer user computing device 140 may display auser interface that includes at least a portion of the first ranked listof space models. For example, the first designer user computing device140 may display a graphical user interface similar to graphical userinterface 300, which is shown in FIG. 3, based on receiving the firstuser interface data from the generative design computing platform 110.As seen in FIG. 3, graphical user interface 300 may include informationidentifying one or more different space models generated by thegenerative design computing platform 110, ranking information indicatingthe rank and/or score of one or more space models, and/or visualinformation indicating graphical views of one or more space modelsgenerated by the generative design computing platform 110 and/orportions thereof. In some instances, the first designer user computingdevice 140 may display such a user interface based on or in response toreceiving the user interface data from the generative design computingplatform 110. Additionally or alternatively, in displaying the userinterface that includes at least a portion of the first ranked list ofspace models, the first designer user computing device 140 may displaythe metric scores and/or aggregate score computed at step 208 (and/orother scores discussed in the examples above).

In some instances, in displaying the user interface that includes atleast a portion of the first ranked list of space models, the firstdesigner user computing device 140 may display each of the space modelsin a grid along with metrics corresponding to each space model (e.g.,based on the user interface data received from the generative designcomputing platform 110). In these instances, in response to receivinguser input selecting a portion of a displayed space model, the firstdesigner user computing device 140 may display a rendering of and/orother graphics associated with one or more work points (e.g., seats) inthe space model, along with calculations of views to the outside fromeach of the one or more work points and/or other metrics associated witheach work point.

Referring to FIG. 2D, at step 213, the generative design computingplatform 110 may receive data indicating a selection of the first spacemodel from the first list of ranked space models. For example, thegenerative design computing platform 110 may receive the data indicatingthe selection of the first space model from the first list of rankedspace models via the communication interface 113 and from the firstdesigner user computing device 140.

At step 214, the generative design computing platform 110 may generate avisual rendering of the first space model. For example, the generativedesign computing platform 110 may generate the visual rendering of thefirst space model in response to or based on receiving the dataindicating the selection of the first space model from the first rankedlist of space models. In some instances, in generating the visualrendering of the first space model, the generative design computingplatform 110 may generate a two-dimensional or three dimensionalrendering of the first space model. In some instances, in generatingsuch a rendering, the generative design computing platform 110 may userendering software built into a drawing tool to convert blocks,settings, furniture, and/or other elements indicated in the space modelinto two-dimensional and/or three-dimensional objects that are viewableby a user and/or that reflect views of the space if the space model wereto be implemented.

At step 215, the generative design computing platform 110 may send thevisual rendering of the first space model to the first designer usercomputing device 140 (e.g., via the communication interface 113). Insome instances, sending the visual rendering of the first space model tothe first designer user computing device 140 may cause the firstdesigner user computing device 140 to display a user interface thatincludes at least a portion of the visual rendering of the first spacemodel. For example, by sending the visual rendering of the first spacemodel to the first designer user computing device 140, the generativedesign computing platform 110 may cause the first designer usercomputing device 140 to display a user interface similar to graphicaluser interface 400, which is shown in FIG. 4 and described below.

In some instances, a user of the first designer user computing device140 may be able to modify parameters of the space model for variousreasons, such as to further refine the space model, to optimizeparameters beyond the calculations made by the generative designcomputing platform 110, and/or to refine the space model to account forsocial distancing requirements. For example, the first designer usercomputing device 140 may display a graphical user interface similar tographical user interface 400, which is shown in FIG. 4. In doing so, thefirst designer user computing device 140 may allow users to modifyvariables that may control, alter, and/or otherwise affect the layout ofa space model and/or other parameters of a space model. For instance,the first designer user computing device 140 may display and/orotherwise present one or more user-selectable controls allowing a userto modify variables such as circulation percentage (which may, e.g.,impact corridor width and/or other parameters affecting circulation ofindividuals within the space), group space percentage (which may, e.g.,impact the relative amount of space allocated to common space), officeand workstation size (which may, e.g., impact the sizes of various workpoints to optimize for social distancing requirements), sharing ratio(which may, e.g., impact whether and to what extent workspaces areconfigured as shared hot spots instead of as reserved desks), and/orother variables.

In these instances, as one or more of the variables are modified, thefirst designer user computing device 140 may show an impact of themodifications (e.g., by displaying updated information indicating howmany people can fit into the office and/or other impacts of the variablemodification). This updated data may, for instance, be determined by thefirst designer user computing device 140, or the first designer usercomputing device 140 may send the modifications to the generative designcomputing platform 110 (which may, e.g., calculate and/or otherwisedetermine the impacts of the variable modification and return dataindicating the impacts of the variable modification to the firstdesigner user computing device 140). In some instances, the firstdesigner user computing device 140 may receive user input correspondingto new building blocks, such as shielding to be deployed between workersand/or other space materials designed with antiviral properties. Thefirst designer user computing device 140 then may send this user inputand/or other information associated with the new building blocks togenerative design computing platform 110, which may incorporate theminto the space model (e.g., by re-generating the space model and/or oneor more other space models, e.g., by re-executing one or more of thesteps described above). Additionally or alternatively, the firstdesigner user computing device 140 may receive user input identifyingone or more pieces of furniture that are already owned by the occupantof the space, and may send this user input and/or other informationassociated with the one or more pieces of furniture that are alreadyowned by the occupant of the space to generative design computingplatform 110. The generative design computing platform 110 then mayincorporate such furniture into the space model (e.g., by re-generatingthe space model and/or one or more other space models, e.g., byre-executing one or more of the steps described above). In this way, thegenerative design computing platform 110 may generate one or more spacemodels indicating potential reconfigurations of already-owned furniture(e.g., to facilitate compliance with new social distancing requirementsin existing spaces, such as in existing office spaces) rather thanproposing new space models that involve purchasing and/or deploying anentirely new suite of furniture.

At step 216, the generative design computing platform 110 may receivedata indicating a user modification of the first space model (e.g., viathe communication interface 113 and from the first designer usercomputing device 140). In some instances, the data indicating the usermodification of the first space model may correspond to a usermodification received by the first designer user computing device 140via the graphical user interface displayed at step 215. For example, atstep 216, the generative design computing platform 110 may receive dataindicating a user modification such as a refinement to the space modeland/or a manual optimization of one or more parameters underlying thespace model, as in the examples discussed above.

Referring to FIG. 2E, at step 217, based on or in response to receivingthe data indicating the user modification of the first space model, thegenerative design computing platform 110 may update a machine learningengine executed on the generative design computing platform 110. Forexample, the generative design computing platform 110 may update and/orretrain the machine learning engine based on the data indicating theuser modification of the first space model. For instance, to the extentthat a user manually refined a layout of the space model and/or manuallyoptimized one or more parameters underlying the space model, suchrefinements and/or optimizations may be captured by the generativedesign computing platform 110 and used to retrain the machine learningengine, so that such refinements and/or optimizations may beautomatically implemented by the generative design computing platform110 when generating future space models. Additionally or alternatively,in updating the machine learning engine, the generative design computingplatform 110 may cause the machine learning engine 112 d toautomatically update the first geometry model and/or metricscorresponding to the first geometry model. For instance, to the extentthat the manual refinements and/or optimizations touch on elements ofthe geometry model and/or its associated metrics, the generative designcomputing platform 110 may update the geometry model and/or the metricscorresponding to the geometry model, so that the refinements and/oroptimizations may be automatically implemented by the generative designcomputing platform 110 when generating future space models based on thesame geometry model.

Subsequently, the generative design computing platform 110 may continueprocessing space program data and/or generating space models for otherphysical spaces, similar to how the generative design computing platform110 may process space program data and generate space models in theexamples discussed above. For example, at step 218, the generativedesign computing platform 110 may receive second space program data(e.g., via the communication interface 113 and from second designer usercomputing device 150). For example, the generative design computingplatform 110 may receive information identifying one or more parametersof a second physical space different from the first physical space. Insome instances, actions performed at step 218 may be similar to thosedescribed above at step 205 with regard to receiving the first spaceprogram data.

At step 219, the generative design computing platform 110 may load asecond geometry model from the database storing the one or more geometrymodels. For example, the generative design computing platform 110 mayload information defining a second plurality of design rules. In someinstances, actions performed at step 219 may be similar to thosedescribed above at step 206 with regard to loading the first geometrymodel. At step 220, the generative design computing platform 110 maygenerate a second plurality of space models for the second physicalspace based on the second space program data and the second geometrymodel. In some instances, actions performed at step 220 may be similarto those described above at step 207 with regard to generating the firstplurality of space models.

Referring to FIG. 2F, at step 221, based on the second geometry model,the generative design computing platform 110 may score the secondplurality of space models. In some instances, in scoring the secondplurality of space models, the generative design computing platform 110may produce a score for each space model of the second plurality ofspace models. In some instances, actions performed at step 221 may besimilar to those described above at step 208 with regard to scoring thefirst plurality of space models. At step 222, the generative designcomputing platform 110 may rank the second plurality of space modelsbased on the score for each space model of the second plurality of spacemodels. In some instances, by ranking the second plurality of spacemodels, the generative design computing platform 110 may produce asecond ranked list of space models. In some instances, actions performedat step 222 may be similar to those described above at step 209 withregard to ranking the first plurality of space models.

At step 223, the generative design computing platform 110 may generatesecond user interface data that includes the second ranked list of spacemodels. In some instances, actions performed at step 222 may be similarto those described above at step 210 with regard to generating the firstuser interface data. At step 224, the generative design computingplatform 110 may send the second user interface data to the seconddesigner user computing device 150 (e.g., via the communicationinterface 113). In some instances, in sending the second user interfacedata to the second designer user computing device 150, the generativedesign computing platform 110 may cause the second user computing deviceto display a user interface that includes at least a portion of thesecond ranked list of space models. In some instances, actions performedat step 224 may be similar to those described above at step 211 withregard to sending the first user interface data.

Referring to FIG. 2G, at step 225, based on the second user interfacedata, the second designer user computing device 150 may display a userinterface that includes at least a portion of the second ranked list ofspace models. In some instances, the second designer user computingdevice 150 may display a graphical user interface similar to graphicaluser interface 500, which is shown in FIG. 5. In some instances, actionsperformed at step 225 may be similar to those described above at step212 with regard to displaying a user interface. For example, as seen inFIG. 5, graphical user interface 500 may include information identifyingone or more different space models generated by the generative designcomputing platform 110 for the second physical space, rankinginformation indicating the rank and/or score of the one or more spacemodels, and/or visual information indicating graphical views of the oneor more space models generated by the generative design computingplatform 110 and/or portions thereof.

Subsequently, the generative design computing platform 110 may receiveand process a request to export one or more space models. As illustratedin greater detail below, in processing such a request, the generativedesign computing platform 110 may export data in various differentformats, using one or more of the multi-platform interoperabilityfeatures described herein. In particular, and as described above (e.g.,with respect to step 207), the generative design computing platform 110may generate each space model of a plurality of space models in aplurality of different data formats (e.g., in a CAD format, a CETformat, a Revit format, a SketchUp format, and/or one or more otherformats), and this multi-format generation may expedite the process bywhich data may be exported in different formats.

For example, at step 226, the generative design computing platform 110may receive data indicating a request to export a space model (e.g., thefirst space model) to a first design tool. In some instances, thegenerative design computing platform 110 may receive the data indicatingthe request to export the space model to the first design tool from thefirst designer user computing device 140 and via the communicationinterface 113. In some instances, in receiving the data indicating therequest to export the space model to the first design tool, thegenerative design computing platform 110 may receive data indicating arequest to export a space model that is defined in a plurality ofdifferent data formats (e.g., in a CAD format, a CET format, a Revitformat, a SketchUp format, and/or one or more other formats) in aspecific format that is compatible with and/or otherwise may beprocessed using the first design tool.

At step 227, in response to receiving the data indicating the request toexport the space model to the first design tool, the generative designcomputing platform 110 may generate one or more first drawing filesbased on the first space model. In some instances, in generating suchdrawing files, the generative design computing platform 110 may select afirst data format of the plurality of data formats (e.g., in which thefirst drawing files should be generated and/or outputted) based on thefirst design tool (e.g., based on the compatibility of the first designtool with different drawing file formats). In these instances, once thefirst data format has been selected, the generative design computingplatform 110 may extract first format-specific data (which may, e.g., bedefined in the first data format) from the first space model. Inparticular, and as discussed above, the first space model may have beeninitially generated in a plurality of different data formats (e.g., in aCAD format, a CET format, a Revit format, a SketchUp format, and/or oneor more other formats). Thus, to generate drawing files from the firstspace model in any particular format, the generative design computingplatform 110 might only need to extract format-specific data from thefirst space model (which may, e.g., provide many technical advantages,such as increased efficiency, reduced processing load, and/or reducedconsumption of network resources). Once the first format-specific datahas been extracted, the generative design computing platform 110 maycreate the one or more first drawing files by writing the firstformat-specific data extracted from the first space model into one ormore new drawing files defined according to the first data format.

At step 228, the generative design computing platform 110 may send theone or more first drawing files to the first designer user computingdevice 140 (e.g., via the communication interface 113). In someinstances, by sending the one or more first drawing files to the firstdesigner user computing device 140, the generative design computingplatform 110 may cause the first designer user computing device 140 todisplay the one or more first drawing files. Referring to FIG. 2H, atstep 229, the first designer user computing device 140 may receive anddisplay the one or more first drawing files.

Subsequently, the generative design computing platform 110 may generateand/or provide one or more user interfaces that enable a customer (e.g.,an occupant of the physical space) to purchase one or more furnitureelements associated with a space model and/or otherwise view and/orimplement the space model. For example, at step 230, the generativedesign computing platform 110 may generate and send one or more commandsdirecting client user computing device 160 to display a graphical userinterface that includes a user-selectable furniture-purchase element. Insome instances, in generating and sending the one or more commandsdirecting the client user computing device 160 to display a graphicaluser interface that includes a user-selectable furniture-purchaseelement, the generative design computing platform 110 may cause theclient user computing device 160 to display a graphical user interfacethat includes a user-selectable furniture-purchase element. For example,the client user computing device 160 may display a graphical userinterface similar to graphical user interface 600, which is shown inFIG. 6. As seen in FIG. 6, graphical user interface 600 may includeinformation about a space model (e.g., metrics, scores, detailsassociated with blocks, settings, and/or furniture, and/or otherinformation), one or more renderings of the space model, and/or one ormore user-selectable options enabling adoption of the space model and/orpurchasing of one or more furniture items associated with the spacemodel. In some instances, graphical user interface 600 may include finalpricing information for the space model (e.g., based on included blocks,settings, furniture, and/or other information), which may be based onpricing information pulled from an internal and/or external data source.For instance, in generating the user interface(s) and/or causing theclient user computing device 160 to display such user interface(s), thegenerative design computing platform 110 may calculate and/or otherwisedetermine cost estimates and/or price estimates indicating a predictedcost of building and/or otherwise implementing the space model. Forexample, the generative design computing platform 110 may calculateand/or otherwise determine an estimated cost of building out thesettings specified in the space model (e.g., based on data maintainedand/or stored by the generative design computing platform 110 indicatingstandard and/or average costs for similar settings in similar spaces).Additionally or alternatively, the generative design computing platform110 may calculate and/or otherwise determine an estimated cost ofpurchasing the one or more specified pieces of furniture in the spacemodel (e.g., based on unit-level pricing data and/or other details,which may, e.g., be retrieved by the generative design computingplatform 110 from another system or database, such as a Harbordatabase).

In some instances, in generating one or more user interfaces associatedwith the space model, the generative design computing platform 110 maydetermine that there is extra space (e.g., positive flex) in the plan ornot enough space (e.g., negative flex) in the plan and may generate suchuser interfaces to indicate and/or otherwise enable interaction withthis positive flex and/or negative flex. Thus, in displaying the one ormore user interfaces associated with the space model, the client usercomputing device 160 may display a space model with positive flex and/ornegative flex. For example, in displaying the space model with positiveflex, the client user computing device 160 may display a floor plan thathas room for additional furniture. In these instances, a user of theclient user computing device 160 (who may e.g., be a designer) mayselect additional furniture to fill the space, and these selections maybe communicated by the client user computing device 160 to thegenerative design computing platform 110, which may update one or moredata records to indicate the selections and/or other changes to thespace model. In displaying the space model with negative flex, theclient user computing device 160 may display furniture that exceeds theavailable space in the floor plan (e.g., a couch and/or other furniturethat exceeds the dimensions for a particular space). In these instances,a user of the client user computing device 160 may expand acorresponding block within the space model to account for any additionalneeded space, and this expansion and/or other associated changes may becommunicated by the client user computing device 160 to the generativedesign computing platform 110, which may update one or more data recordsto indicate the expansion and/or other changes to the space model. Insome instances, rather than presenting the option to reconfigurefurniture to a user, a computing device (such as the client usercomputing device 160) may mimic the flexibility of a designer, andautomatically modify the floor plan accordingly based on availablespace. In some instances, in displaying the positive and/or negativeflex, the client user computing device 160 may display a graphical userinterface similar to graphical user interface 800, which is shown inFIG. 8.

At step 231, the generative design computing platform 110 may receivefurniture selection information indicating an order for one or more ofthe furniture items. For example, the furniture selection informationmay be based on a user input received via the graphical user interfacedisplayed by the client user computing device 160 at step 230, and maybe sent to the generative design computing platform 110 from the clientuser computing device 160. At step 232, the generative design computingplatform 110 may process the order for the one or more furniture itemsspecified in the furniture selection information received at step 231.For example, the generative design computing platform 110 may cause theone or more furniture items to be purchased and sent to an addressspecified by a user of the client user computing device 160.

Subsequently, the generative design computing platform 110 may repeatone or more steps of the example sequence discussed above in generatingother geometry models, generating other space models, and/or outputtingother drawing files associated with various space models. In addition,the generative design computing platform 110 may continuously update itsmachine learning engine 112 d based on user input and/or other datareceived by generative design computing platform 110, so as tocontinuously and automatically optimize the generation of geometrymodels and space models.

In some instances, user applications may be designed and implementedthat integrate with the features described in steps 201-231, which mayallow for further customization and functionality beyond that describedabove. For example, one or more of the features described above mayhosted on and/or provided by a cloud-based software-as-a-service (SaaS)platform on top of which various designers and/or developers may buildcustomized applications for use by themselves or others. Thesecustomized applications may, for instance, be hosted on the generativedesign computing platform 110 or on different and/or external computingplatforms. In some instances, these customized applications mayintegrate with, use, and/or replace functionality and/or features of thetools described above. For instance, any and/or all aspects of acustomized application may be presented as additional or alternativeoptions in a setting selector tool, which may be executed on and/orintegrated with the generative design computing platform 110.

In some instances, any and/or all of the data that is generated and/orused by the generative design computing platform 110 may be storedand/or otherwise maintained in a single, centralized project asset anddesigner database. Such a database may, for example, also include itemsfrom other sources, such as salesforce data and/or scout data. In somearrangements, such a centralized database may be made up of multipletables and/or subsidiary databases, such as a project asset database(which may, e.g., store data in connection with specific projects, suchas space models and/or other items for specific projects), a designerdatabase (which may, e.g., store designer preferences), and a settingsvault (which may, e.g., store data about specific furniture items and/ormay connect to one or more external databases, such as Herman Miller'sHarbour database).

FIG. 7 depicts an illustrative method for generating space models andgeometry models using a machine learning system with multi-platforminterfaces in accordance with one or more example embodiments. Referringto FIG. 7, at step 705, a computing platform having at least oneprocessor, a communication interface, and memory may receive one or moredrawing models. At step 710, the computing platform may identify designparameters based on the one or more drawing models. At step 715, basedon the design parameters, the computing platform may generate one ormore geometry models. At step 720, the computing platform may store thegeometry models. At step 725, the computing platform may receive spaceprogram data. At step 730, the computing platform may load one or moregeometry models based on the space program data. At step 735, thecomputing platform may generate one or more space models based on theone or more geometry models and the space program data. At step 740, thecomputing platform may store the one or more space models. At step 745,the computing platform may score the one or more space models, and rankthe one or more space models based on the scores. At step 750, thecomputing platform may send user interface data to a designer usercomputing device, which may cause the designer user computing device todisplay a graphical user interface that includes a ranked list of theone or more space models. At step 755, the computing platform mayreceive model selection data indicating selection of a first spacemodel. At step 760, the computing platform may generate a visualrendering of the first space model and send the visual rendering to thedesigner user computing device. At step 765, the computing platform maydetermine whether or not data indicating a modification to the firstspace model was received. If not, the computing platform may proceed tostep 775. If data indicating a modification to the first space model wasreceived, the computing platform may proceed to step 770.

At step 770, the computing platform may update a machine learning engineused to generate the geometry models and/or the space models. At step775, the computing platform may determine whether or not data requestingexport of the first space model was received. If not, the method mayend. If data requesting export of the first space model was received,the computing platform may proceed to step 780.

At step 780, the computing platform may send one or more drawing filesbased on the first space model in response to the export request. Atstep 785, the computing platform may send one or more commands directinga client user computing device to display a user interface that mayprompt a user to select furniture (e.g., from the first space model) forpurchase. At step 790, the computing platform may identify whether ornot furniture selection data was received. If not, the method may end.If furniture selection data was received, the computing platform mayproceed to step 795. At step 795, the computing platform may process anorder corresponding to the furniture selection data.

FIGS. 9A-9B depict an illustrative event sequence for providingworkplace configuration interfaces in accordance with one or moreexample embodiments. Actions described in FIGS. 9A-9B may be performedin addition or as an alternative to the space program methods describedabove with regard to providing insights for a particular project.Referring to FIG. 9A, at step 901, first designer user computing device140 may display a graphical user interface that enables input ofdepartment information, and may receive the department informationthrough the graphical user interface. For example, the first designeruser computing device 140 may receive input indicating which departmentsshould be included in a final workplace configuration (e.g., executive,legal, finance, sales, marketing, communications, design, and/or otherdepartments).

At step 902, the first designer user computing device 140 may display agraphical user interface that enables input of position information, andmay receive the position information through the graphical userinterface. For example, the first designer user computing device 140 mayreceive input indicating which positions correspond to the variousdepartments (e.g., c-suite, executive, vice president, director,manager, staff, and/or other positions).

At step 903, the first designer user computing device 140 may display agraphical user interface that enables input of headcount information,and may receive the headcount information through the graphical userinterface. For example, the first designer user computing device 140 mayreceive input indicating a number of employees at each position(identified at step 902) for each department (identified at step 901).As a particular example, the first designer user computing device 140may receive input indicating that the legal department has twoexecutives.

At step 904, the first designer user computing device 140 maycommunicate with the generative design computing platform 110 to sharethe information received at steps 901-903 (e.g., the department,position, and headcount information). At step 905, the generative designcomputing platform 110 may receive this information sent at step 904.

Referring to FIG. 9B, the generative design computing platform 110 mayuse the information received at step 905 to generate a workpointconfiguration interface (or information that may be used to generate theworkpoint configuration interface). For example, the generative designcomputing platform 110 may identify a total quantity of employees ateach position, a corresponding default workspace for each position(e.g., office vs. workstation and it's corresponding size), squarefootage of a single corresponding workspace (e.g., a single office,workstation, or the like for the particular position), and squarefootage of the workspaces for all employees at each position (e.g.,total square footage occupied by seven chief operating officers, eachhaving an office of 275 square feet, is 1925 square feet). Afteridentifying these metrics, the generative design computing platform 110may identify a total square footage occupied by all anticipatedemployees across all positions/departments (e.g., by adding all of theidentified total square footages for each role), and may then add groupspace size (e.g., meeting rooms, open collaborative rooms, and/or othergroup spaces), support space size (e.g., printer/copy areas, closets,local area network rooms, and/or other support spaces), and/or any othersquare footage to reach a total usable area square footage.

In some instances, in generating the workpoint configuration interface,the generative design computing platform 110 may include controls thatmay allow modification of breakpoint (e.g., at what position in ahierarchy are employees assigned an office vs. workstation), workpointsize (e.g., office or workstation size), and/or other parameters. Forexample, this may allow a user to modify a workpoint configuration sothat only employees who are managers or above may have an office ratherthan employees who are supervisors and above. In doing so, the user mayreduce a square footage occupied by employees at the supervisor level bymoving them from offices to workstations. Additionally or alternatively,the workpoint configuration may be modified so as to reduce office sizeof employees at a particular role to reduce the square footage occupiedby those employees. Conversely, a user may modify the workpointconfiguration to increase a number of employees occupying offices and/orincrease individual office sizes if there is extra usable area.

In some instances, the generative design computing platform 110 maygenerate one or more workpoint configuration options that include theinformation described above, and may include each option on theworkpoint configuration interface. In this way, a user may identifywhich option is most desirable, and may modify that option as necessary.

By automatically generating these workpoint configuration options, thegenerative design computing platform 110 may conserve significantamounts of time that may otherwise be consumed by designing a completetest fit for a particular space and subsequently refining the fit asnecessary based on whether or not it exceeds a total usable area of thespace, does not use all of the total usable area of the space, and/orother factors.

In some instances, the generative design computing platform 110 may alsogenerate cost estimates for each workplace configuration (e.g., based ona price per square footage from a corresponding lease and the identifiedtotal square footage for each workplace configuration). In doing so, thegenerative design computing platform 110 may enable a user to identifycost savings associated with each workplace configuration (e.g., thecost savings associated with a smaller space as compared to a largerspace).

At step 907, the generative design computing platform 110 may send theworkpoint configuration interface to the first designer user computingdevice 140 for display. At step 908, the first designer user computingdevice 140 may receive the workpoint configuration interface.

At step 909, the first designer user computing device 140 may displaythe workpoint configuration interface. For example, the first designeruser computing device 140 may display a graphical user interface similarto graphical user interface 1000, which is illustrated in FIG. 10. Forexample, the first designer user computing device 140 may display agraphical user interface that enables a user to adjust workplaceconfigurations as described above at step 906, and to observe thecorresponding cost savings.

Although the above described systems, methods, and event sequenceprimarily illustrate a use case involving commercial office design, theymay similarly apply to other use cases such as residential design,outdoor design, manufacturing facilities, or the like without departingfrom the scope of the disclosure. For example, the generative designcomputing platform 110 may execute one or more steps similar to thosedescribed above in generating space models for residential spaces,outdoor spaces, manufacturing facility spaces, and/or other types ofspaces.

One or more aspects of the disclosure may be embodied in computer-usabledata or computer-executable instructions, such as in one or more programmodules, executed by one or more computers or other devices to performthe operations described herein. Program modules may include routines,programs, objects, components, data structures, and the like thatperform particular tasks or implement particular abstract data typeswhen executed by one or more processors in a computer or other dataprocessing device. The computer-executable instructions may be stored ascomputer-readable instructions on a computer-readable medium such as ahard disk, optical disk, removable storage media, solid-state memory,RAM, and the like. The functionality of the program modules may becombined or distributed as desired in various embodiments. In addition,the functionality may be embodied in whole or in part in firmware orhardware equivalents, such as integrated circuits, application-specificintegrated circuits (ASICs), field programmable gate arrays (FPGA), andthe like. Particular data structures may be used to more effectivelyimplement one or more aspects of the disclosure, and such datastructures are contemplated to be within the scope of computerexecutable instructions and computer-usable data described herein.

One or more aspects described herein may be embodied as a method, anapparatus, or as one or more computer-readable media storingcomputer-executable instructions. Accordingly, those aspects may takethe form of an entirely hardware embodiment, an entirely softwareembodiment, an entirely firmware embodiment, or an embodiment combiningsoftware, hardware, and firmware aspects in any combination. Inaddition, various signals representing data or events as describedherein may be transferred between a source and a destination in the formof light or electromagnetic waves traveling through signal-conductingmedia such as metal wires, optical fibers, or wireless transmissionmedia (e.g., air or space). The one or more computer-readable media maybe and/or include one or more non-transitory computer-readable media.

As described herein, the various methods and acts may be operativeacross one or more computing servers and one or more networks. Thefunctionality may be distributed in any manner, or may be located in asingle computing device (e.g., a server, a client computer, and thelike). For example, in alternative embodiments, one or more of thecomputing platforms discussed above may be combined into a singlecomputing platform, and the various functions of each computing platformmay be performed by the single computing platform. In such arrangements,any and/or all of the above-discussed communications between computingplatforms may correspond to data being accessed, moved, modified,updated, and/or otherwise used by the single computing platform.Additionally or alternatively, one or more of the computing platformsdiscussed above may be implemented in one or more virtual machines thatare provided by one or more physical computing devices. In sucharrangements, the various functions of each computing platform may beperformed by the one or more virtual machines, and any and/or all of theabove-discussed communications between computing platforms maycorrespond to data being accessed, moved, modified, updated, and/orotherwise used by the one or more virtual machines.

Aspects of the disclosure have been described in terms of illustrativeembodiments thereof. Numerous other embodiments, modifications, andvariations within the scope and spirit of the appended claims will occurto persons of ordinary skill in the art from a review of thisdisclosure. For example, one or more of the steps depicted in theillustrative figures may be performed in other than the recited order,and one or more depicted steps may be optional in accordance withaspects of the disclosure.

What is claimed is:
 1. A computing platform, comprising: at least oneprocessor; a communication interface; and memory storingcomputer-readable instructions that, when executed by the at least oneprocessor, cause the computing platform to: receive, via thecommunication interface, from a data server, a plurality of drawingmodels corresponding to different space designs; identify a plurality ofdesign parameters associated with each drawing model of the plurality ofdrawing models corresponding to the different space designs; train amachine learning engine based on the plurality of drawing modelscorresponding to the different space designs and the plurality of designparameters associated with each drawing model of the plurality ofdrawing models corresponding to the different space designs, whereintraining the machine learning engine produces at least one geometrymodel corresponding to the plurality of drawing models; and store, in adatabase storing one or more additional geometry models, the at leastone geometry model corresponding to the plurality of drawing models. 2.The computing platform of claim 1, wherein receiving the plurality ofdrawing models corresponding to the different space designs comprisesreceiving at least one two-dimensional computer-aided design (CAD) modelor PDF drawing.
 3. The computing platform of claim 1, whereinidentifying the plurality of design parameters associated with eachdrawing model of the plurality of drawing models corresponding to thedifferent space designs comprises identifying a plurality of designfeatures, and wherein the plurality of design features comprises one ormore of: a total square footage, a total number of offices, a totalnumber of meeting spaces, a total number of community spaces, a numberof seats per office, a number of seats per meeting space, a number ofseats per community space, a percentage of the total square footageallocated to offices, a percentage of the total square footage allocatedto meeting spaces, a percentage of the total square footage allocated tocommunity space, an average office size, or an average meeting spacesize.
 4. The computing platform of claim 3, wherein identifying theplurality of design parameters associated with each drawing model of theplurality of drawing models corresponding to the different space designscomprises, prior to identifying the plurality of design parameters,selecting the plurality of design features by applying cognitive machinelearning based on an organization corresponding to each drawing model ofthe plurality of drawing models.
 5. The computing platform of claim 4,wherein selecting the plurality of design features comprises selectingthe plurality of design features based on one or more of: an industry,geographic data, a size, or a personality of the organization.
 6. Thecomputing platform of claim 3, wherein selecting the plurality of designfeatures comprises selecting the plurality of design features based on auser input and wherein the plurality of design features are consistentfor each drawing model of the plurality of drawing models.
 7. Thecomputing platform of claim 1, wherein producing the at least onegeometry model comprises identifying one or more design rules that areapplicable to score compliance of at least one space model with theplurality of drawing models, and wherein the one or more design rulescomprise one or more of: data ranges or numerical constraints.
 8. Thecomputing platform of claim 7, wherein the memory stores additionalcomputer-readable instructions that, when executed by the at least oneprocessor, cause the computing platform to: receive, via thecommunication interface, from a user computing device, space programdata identifying one or more parameters of a physical space; load the atleast one geometry model from the database storing the one or moreadditional geometry models; generate a plurality of space models for thephysical space based on the space program data identifying the one ormore parameters of the physical space and the at least one geometrymodel; score, based on the at least one geometry model, the plurality ofspace models generated for the physical space, wherein scoring theplurality of space models generated for the physical space produces ascore for each space model of the plurality of space models; rank theplurality of space models generated for the physical space based on thescore for each space model of the plurality of space models, whereinranking the plurality of space models generated for the physical spaceproduces a ranked list of space models; generate user interface datacomprising the ranked list of space models; and send, via thecommunication interface and to the user computing device, the userinterface data comprising the ranked list of space models, whereinsending the user interface data comprising the ranked list of spacemodels to the user computing device causes the user computing device todisplay a user interface comprising at least a portion of the rankedlist of space models.
 9. The computing platform of claim 8, whereingenerating the plurality of space models for the physical space based onthe space program data identifying the one or more parameters of thephysical space and the at least one geometry model comprises: generatinga plurality of block models for the physical space; scoring theplurality of block models generated for the physical space based on theat least one geometry model, wherein scoring the plurality of blockmodels generated for the physical space produces a score for each blockmodel of the plurality of block models; selecting a subset of theplurality of block models based on the score for each block model of theplurality of block models; generating a plurality of settings models forthe physical space, wherein each settings model of the plurality ofsettings models corresponds to a particular block model of the subset ofthe plurality of block models; scoring the plurality of settings modelsgenerated for the physical space based on the at least one geometrymodel, wherein scoring the plurality of settings models generated forthe physical space produces a score for each settings model of theplurality of settings models; selecting a subset of the plurality ofsettings models based on the score for each settings model of theplurality of settings models; generating a plurality of furniture modelsfor the physical space, wherein each furniture model of the plurality offurniture models corresponds to a particular settings model of thesubset of the plurality of settings models; scoring the plurality offurniture models generated for the physical space based on the at leastone geometry model, wherein scoring the plurality of furniture modelsgenerated for the physical space produces a score for each furnituremodel of the plurality of furniture models; and selecting a subset ofthe plurality of furniture models based on the score for each furnituremodel of the plurality of furniture models, wherein the subset of theplurality of furniture models corresponds to the plurality of spacemodels generated for the physical space.
 10. The computing platform ofclaim 9, wherein each block model of the plurality of block modelsindicates potential locations of different neighborhoods in the physicalspace, each settings model of the plurality of settings models indicatespotential locations of different work settings in the physical space,and each furniture model of the plurality of furniture models indicatespotential locations of different furniture items in the physical space.11. A method, comprising: at a computing platform comprising at leastone processor, a communication interface, and memory: receiving, by theat least one processor, via the communication interface, from a dataserver, a plurality of drawing models corresponding to different spacedesigns; identifying, by the at least one processor, a plurality ofdesign parameters associated with each drawing model of the plurality ofdrawing models corresponding to the different space designs; training,by the at least one processor, a machine learning engine based on theplurality of drawing models corresponding to the different space designsand the plurality of design parameters associated with each drawingmodel of the plurality of drawing models corresponding to the differentspace designs, wherein training the machine learning engine produces atleast one geometry model corresponding to the plurality of drawingmodels; and storing, by the at least one processor and in a databasestoring one or more additional geometry models, the at least onegeometry model corresponding to the plurality of drawing models.
 12. Themethod of claim 11, wherein receiving the plurality of drawing modelscorresponding to the different space designs comprises receiving, by theat least one processor, at least one two-dimensional computer-aideddesign (CAD) model.
 13. The method of claim 11, wherein identifying theplurality of design parameters associated with each drawing model of theplurality of drawing models corresponding to the difference spacedesigns comprises identifying, by the at least one processor, aplurality of design features, and wherein the plurality of designfeatures comprises one or more of: a total square footage, a totalnumber of offices, a total number of meeting spaces, a total number ofcommunity spaces, a number of seats per office, a number of seats permeeting space, a number of seats per community space, a percentage ofthe total square footage allocated to offices, a percentage of the totalsquare footage allocated to meeting spaces, a percentage of the totalsquare footage allocated to community space, an average office size, oran average meeting space size.
 14. The method of claim 13, whereinidentifying the plurality of design parameters associated with eachdrawing model of the plurality of drawing models corresponding to thedifferent space designs comprises, prior to identifying the plurality ofdesign parameters, selecting, by the at least one processor, theplurality of design features by applying cognitive machine learningbased on an organization corresponding to each drawing model of theplurality of drawing models.
 15. The method of claim 14, whereinselecting the plurality of design features comprises selecting, by theat least one processor, the plurality of design features based on one ormore of: an industry, geographic data, a size, or a personality of theorganization.
 16. The method of claim 13, wherein selecting theplurality of design features comprises selecting, by the at least oneprocessor, the plurality of design features based on a user input andwherein the plurality of design features are consistent for each drawingmodel of the plurality of drawing models.
 17. The method of claim 11,wherein producing the at least one geometry model comprises identifying,by the at least one processor, one or more design rules that areapplicable to score compliance of at least one space model with theplurality of drawing models, and wherein the one or more design rulescomprise one or more of: data ranges or numerical constraints.
 18. Themethod of claim 17, further comprising: receiving, by the at least oneprocessor and via the communication interface, from a user computingdevice, space program data identifying one or more parameters of aphysical space; loading, by the at least one processor, the at least onegeometry model from the database storing the one or more additionalgeometry models; generating, by the at least one processor, a pluralityof space models for the physical space based on the space program dataidentifying the one or more parameters of the physical space and the atleast one geometry model; scoring, by the at least one processor, basedon the at least one geometry model, the plurality of space modelsgenerated for the physical space, wherein scoring the plurality of spacemodels generated for the physical space produces a score for each spacemodel of the plurality of space models; ranking, by the at least oneprocessor, the plurality of space models generated for the physicalspace based on the score for each space model of the plurality of spacemodels, wherein ranking the plurality of space models generated for thephysical space produces a ranked list of space models; generating, bythe at least one processor, user interface data comprising the rankedlist of space models; and sending, by the at least one processor, viathe communication interface, to the user computing device, the userinterface data comprising the ranked list of space models, whereinsending the user interface data comprising the ranked list of spacemodels to the user computing device causes the user computing device todisplay a user interface comprising at least a portion of the rankedlist of space models.
 19. The method of claim 18, wherein generating theplurality of space models for the physical space based on the spaceprogram data identifying the one or more parameters of the physicalspace and the at least one geometry model comprises: generating, by theat least one processor a plurality of block models for the physicalspace; scoring, by the at least one processor, the plurality of blockmodels generated for the physical space based on the at least onegeometry model, wherein scoring the plurality of block models generatedfor the physical space produces a score for each block model of theplurality of block models; selecting, by the at least one processor, asubset of the plurality of block models based on the score for eachblock model of the plurality of block models; generating, by the atleast one processor, a plurality of settings models for the physicalspace, wherein each settings model of the plurality of settings modelscorresponds to a particular block model of the subset of the pluralityof block models; scoring, by the at least one processor, the pluralityof settings models generated for the physical space based on the atleast one geometry model, wherein scoring the plurality of settingsmodels generated for the physical space produces a score for eachsettings model of the plurality of settings models; selecting, by the atleast one processor, a subset of the plurality of settings models basedon the score for each settings model of the plurality of settingsmodels; generating, by the at least one processor, a plurality offurniture models for the physical space, wherein each furniture model ofthe plurality of furniture models corresponds to a particular settingsmodel of the subset of the plurality of settings models; scoring, by theat least one processor, the plurality of furniture models generated forthe physical space based on the at least one geometry model, whereinscoring the plurality of furniture models generated for the physicalspace produces a score for each furniture model of the plurality offurniture models; and selecting, by the at least one processor, a subsetof the plurality of furniture models based on the score for eachfurniture model of the plurality of furniture models, wherein the subsetof the plurality of furniture models corresponds to the plurality ofspace models generated for the physical space.
 20. One or morenon-transitory computer-readable media storing instructions that, whenexecuted by a computing platform comprising at least one processor, acommunication interface, and memory, cause the computing platform to:receive, via the communication interface, from a data server, aplurality of drawing models corresponding to different space designs;identify a plurality of design parameters associated with each drawingmodel of the plurality of drawing models corresponding to the differentspace designs; train a machine learning engine based on the plurality ofdrawing models corresponding to the different space designs and theplurality of design parameters associated with each drawing model of theplurality of drawing models corresponding to the different spacedesigns, wherein training the machine learning engine produces at leastone geometry model corresponding to the plurality of drawing models; andstore, in a database storing one or more additional geometry models, theat least one geometry model corresponding to the plurality of drawingmodels.